Family-Professional Collaboration on Modified Ride-on Car Intervention for Young Children: Two Case Reports
Bibliographic record
Abstract
The study aimed to describe the implementation of a collaborative ride-on car (ROC) intervention by applying a practice model of family-professional collaboration. The model involves specific strategies for collaboration, “visualizing a preferred future” and “scaling questions.” The participants were two young children with mobility limitations and their mothers. The 12-week of ROC intervention involved training sessions with a therapist and home sessions. The outcomes included the Canadian Occupational Performance Measure (COPM) and Goal Attainment Scaling (GAS). The collaborative strategies facilitated parent engagement in goal setting, planning, and evaluation. After the intervention, the mothers’ ratings of their children’s performance and parent satisfaction on the COPM increased by 6 and 3 points, respectively, and the level of goal attainment exceeded expectations (+1 on GAS) in both families. Prior to the ROC intervention, both families were hesitant to use powered mobility. However, the experience of participating in the ROC intervention process broadened parents’ perspectives on self-directed mobility and led them to explore options for their children to move independently. The collaborative ROC intervention can be used as an intervention for early mobility and a bridging step for families reluctant to use a powered wheelchair.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".